Stochastic policy design in a learning environment with rational expectations

In this paper, we present a method for using rational expectations in a stochastic linear-quadratic optimization framework in which the unknown parameters are updated through a learning scheme. We use the QZ decomposition as suggested by Sims (Ref. 1) to solve the rational expectations part of the m...

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Bibliographic Details
Published in:Journal of optimization theory and applications 2000-06, Vol.105 (3), p.509-520
Main Authors: AMMAN, H. M, KENDRICK, D. A
Format: Article
Language:English
Subjects:
Applied sciences
Decomposition
Economics
Eigenvalues
Exact sciences and technology
General aspects
Macroeconomics
Operational research and scientific management
Operational research. Management science
Optimization
Optimization. Search problems
School environment
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Summary:In this paper, we present a method for using rational expectations in a stochastic linear-quadratic optimization framework in which the unknown parameters are updated through a learning scheme. We use the QZ decomposition as suggested by Sims (Ref. 1) to solve the rational expectations part of the model. The parameter updating is done with the Kalman filter and the optimal control is calculated using the covariance matrix of the uncertain parameter.
ISSN:0022-3239
1573-2878
DOI:10.1023/A:1004620021587